






'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase						:						Dict  =					logging.get_logger(__name__)

_UpperCamelCase						:						List[str]  =					{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_UpperCamelCase						:						int  =					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def __UpperCAmelCase   (					A					:			Any			,       A					:			str			,       A					:			Optional[Any]			,       A					:			Tuple			,       A					:			List[str]		)    ->   Optional[Any]:
			for attribute in key.split('''.'''		):
						UpperCAmelCase_      :       Tuple          =       getattr(a_			,       a_		)

			if weight_type is not None:
						UpperCAmelCase_      :       List[str]          =       getattr(a_			,       a_		).shape
			else:
						UpperCAmelCase_      :       Tuple          =       hf_pointer.shape

			assert hf_shape == value.shape, (
			    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
			    F" {value.shape} for {full_name}"
			)

			if weight_type == "weight":
						UpperCAmelCase_      :       Dict          =       value
			elif weight_type == "weight_g":
						UpperCAmelCase_      :       List[Any]          =       value
			elif weight_type == "weight_v":
						UpperCAmelCase_      :       Optional[Any]          =       value
			elif weight_type == "bias":
						UpperCAmelCase_      :       Union[str, Any]          =       value
			else:
						UpperCAmelCase_      :       Optional[int]          =       value

			logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Dict		)    ->   Any:
			UpperCAmelCase_      :       int          =       []
			UpperCAmelCase_      :       Tuple          =       fairseq_model.state_dict()

			UpperCAmelCase_      :       int          =       hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			UpperCAmelCase_      :       Union[str, Any]          =       None

			for name, value in fairseq_dict.items():
						UpperCAmelCase_      :       Tuple          =       False
						if "conv_layers" in name:
									load_conv_layer(
									    a_			,       a_			,       a_			,       a_			,       hf_model.config.feat_extract_norm == '''group'''			,       )
									UpperCAmelCase_      :       Union[str, Any]          =       True
						elif name.split('''.'''		)[0] == "proj":
									UpperCAmelCase_      :       Optional[Any]          =       fairseq_model.proj
									UpperCAmelCase_      :       Any          =       True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
															UpperCAmelCase_      :       str          =       True
															if "*" in mapped_key:
																		UpperCAmelCase_      :       Union[str, Any]          =       name.split(a_		)[0].split('''.'''		)[-2]
																		UpperCAmelCase_      :       List[Any]          =       mapped_key.replace('''*'''			,       a_		)
															if "weight_g" in name:
																		UpperCAmelCase_      :       Dict          =       '''weight_g'''
															elif "weight_v" in name:
																		UpperCAmelCase_      :       Tuple          =       '''weight_v'''
															elif "bias" in name:
																		UpperCAmelCase_      :       List[Any]          =       '''bias'''
															elif "weight" in name:
																		UpperCAmelCase_      :       int          =       '''weight'''
															else:
																		UpperCAmelCase_      :       List[str]          =       None
															set_recursively(a_			,       a_			,       a_			,       a_			,       a_		)
												continue
						if not is_used:
									unused_weights.append(a_		)

			logger.warning(F"Unused weights: {unused_weights}"		)

			return proj_weight
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			int			,       A					:			Tuple			,       A					:			Union[str, Any]			,       A					:			Tuple		)    ->   Dict:
			UpperCAmelCase_      :       str          =       full_name.split('''conv_layers.'''		)[-1]
			UpperCAmelCase_      :       List[str]          =       name.split('''.'''		)
			UpperCAmelCase_      :       Union[str, Any]          =       int(items[0]		)
			UpperCAmelCase_      :       Dict          =       int(items[1]		)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
									)
									UpperCAmelCase_      :       List[Any]          =       value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
									)
									UpperCAmelCase_      :       Any          =       value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
									    " found."
									)
									UpperCAmelCase_      :       Union[str, Any]          =       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F"{full_name} has size {value.shape}, but"
									    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
									)
									UpperCAmelCase_      :       str          =       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(a_		)
def __UpperCAmelCase   (					A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       emb.weight.shape
			UpperCAmelCase_      :       str          =       nn.Linear(a_			,       a_			,       bias=a_		)
			UpperCAmelCase_      :       List[str]          =       emb.weight.data
			return lin_layer
def __UpperCAmelCase   (					A					:			Union[str, Any]		)    ->   Optional[int]:
			with open(a_			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
						UpperCAmelCase_      :       Any          =       [line.split(''' '''		)[0] for line in lines]

			UpperCAmelCase_      :       List[Any]          =       len(a_		)

			UpperCAmelCase_      :       str          =       {
			    '''<s>''': 0,
			    '''<pad>''': 1,
			    '''</s>''': 2,
			    '''<unk>''': 3,
			}

			vocab_dict.update(dict(zip(a_			,       range(4			,       num_words + 4		)		)		)		)
			return vocab_dict
@torch.no_grad()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[int]			,       A					:			int			,       A					:			Dict			,       A					:			List[str]			,       A					:			Union[str, Any]			,       A					:			Union[str, Any]			,       )    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       WavaVecaConfig.from_pretrained(a_		)
			UpperCAmelCase_      :       Tuple          =       SpeechaTextaConfig.from_pretrained(
			    a_			,       vocab_size=a_			,       decoder_layers=a_			,       do_stable_layer_norm=a_		)

			UpperCAmelCase_      :       str          =       WavaVecaFeatureExtractor(
			    feature_size=1			,       sampling_rate=1_6_0_0_0			,       padding_value=0			,       do_normalize=a_			,       return_attention_mask=a_			,       )

			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]			,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
			UpperCAmelCase_      :       Any          =       model[0].eval()

			# set weights for wav2vec2 encoder
			UpperCAmelCase_      :       List[Any]          =       WavaVecaModel(a_		)
			UpperCAmelCase_      :       Tuple          =       recursively_load_weights_wavaveca(model.encoder			,       a_		)

			UpperCAmelCase_      :       str          =       SpeechaTextaForCausalLM(a_		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,       strict=a_		)

			# set output linear layer
			unexpected_keys.remove('''embed_out'''		)
			UpperCAmelCase_      :       Dict          =       nn.Parameter(model.decoder.embed_out.detach()		)

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"		)
			logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"		)

			UpperCAmelCase_      :       int          =       SpeechEncoderDecoderModel(encoder=a_			,       decoder=a_		)
			UpperCAmelCase_      :       List[str]          =       False

			# add projection layer
			UpperCAmelCase_      :       Optional[Any]          =       nn.Parameter(projection_layer.weight		)
			UpperCAmelCase_      :       Dict          =       nn.Parameter(projection_layer.bias		)

			UpperCAmelCase_      :       Optional[int]          =       create_vocab_dict(a_		)

			with open(os.path.join(a_			,       '''vocab.json'''		)			,       '''w'''		) as fp:
						json.dump(a_			,       a_		)

			UpperCAmelCase_      :       Dict          =       SpeechaTextaTokenizer(os.path.join(a_			,       '''vocab.json'''		)		)
			tokenizer.save_pretrained(a_		)

			UpperCAmelCase_      :       Optional[int]          =       hf_wavavec.config.to_dict()
			UpperCAmelCase_      :       str          =       tokenizer.pad_token_id
			UpperCAmelCase_      :       Optional[int]          =       tokenizer.bos_token_id
			UpperCAmelCase_      :       Optional[Any]          =       tokenizer.eos_token_id
			UpperCAmelCase_      :       int          =       '''speech_to_text_2'''
			UpperCAmelCase_      :       List[Any]          =       '''wav2vec2'''

			UpperCAmelCase_      :       str          =       SpeechEncoderDecoderConfig.from_dict(a_		)

			hf_wavavec.save_pretrained(a_		)
			feature_extractor.save_pretrained(a_		)


if __name__ == "__main__":
						_UpperCamelCase						:						str  =					argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
						parser.add_argument(
						    '--encoder_config_path',
						    default='facebook/wav2vec2-large-lv60',
						    type=str,
						    help='Path to hf encoder wav2vec2 checkpoint config',
						)
						parser.add_argument(
						    '--decoder_config_path',
						    default='facebook/s2t-small-mustc-en-fr-st',
						    type=str,
						    help='Path to hf decoder s2t checkpoint config',
						)
						parser.add_argument('--vocab_size', default=10_224, type=int, help='Vocab size of decoder')
						parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

						_UpperCamelCase						:						Tuple  =					parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    vocab_size=args.vocab_size,
						    num_decoder_layers=args.num_decoder_layers,
						)








'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



